Medical VLMs
VLM: Vision-Language Model
Overview
Medical vision-language models study how to link images such as X-rays, CT scans, pathology slides, and surgical videos with clinical language. Common approaches include CLIP-style image-text pretraining, adapting large multimodal language models with medical data, and adding region-level grounding, segmentation, or retrieval of clinical evidence. They are evaluated on tasks such as report generation on MIMIC-CXR, visual question answering on VQA-RAD and SLAKE, and volumetric CT or whole-slide pathology analysis. A central challenge is ensuring that answers genuinely use relevant visual evidence and remain calibrated under prompt changes, rare findings, and shifts between hospitals or populations.
Latest papers
479 papers51 papers in the last four weeks, up 21% on the four weeks before. 0.3% of all new papers.